ledyard public schools ap statistics curriculum · molesky, jason m., et al. strive for a 5:...

Approved by Instructional Council April 10, 2019

Ledyard Public Schools

AP Statistics Curriculum COURSE DESCRIPTION

The purpose of AP Statistics is to introduce students to the major concepts and tools for collecting, analyzing and

drawing conclusions from data. Students are exposed to four broad conceptual themes:

1. Exploring Data: Describing patterns and departures from patterns

2. Sampling and Experimentations: Planning and conducting a study

3. Anticipating Patterns: Exploring random phenomena using probability and simulation

4. Statistical Inference: Estimating population parameters and testing hypotheses

According to College Board, upon entering this course students are expected to have mathematical maturity and

quantitative reasoning ability. Mathematical maturity could be defined as a complete working knowledge of the

graphical and algebraic concepts through mathematical analysis, including linear, quadratic, exponential and

logarithmic functions. As such, successful completion of Algebra II is a pre-requisite for enrollment.

The College Board also expects that students will learn how to leverage technology in order to explore and analyze data.

As such, all students will also have access to a Ti-84 to enhance the analysis of data during classroom instruction, at

home practice, and on the AP Exam. The calculator will be utilized on a daily basis throughout the course, as well as

being supplemented with instruction on utilizing Google Sheets, Microsoft Excel, and other online apps to further

enhance the analysis of data and create statistical displays.

COURSE TEXTBOOK

Starnes, Daren S., and Josh Tabor. The Practice of Statistics: For the AP® Exam. 6th ed., New York, Bedford,

freeman & worth, 2018.

SUPPLEMENTAL RESOURCES Bock, David E., et al. Stats in Your World. 2nd ed., Boston, Pearson, 2016.

Molesky, Jason M., et al. Strive for a 5: Preparing for the AP Statistics Examination : to Accompany the Practice of

Statistics, Sixth Edition, Daren Starnes, Josh Tabor, Dan Yates, David Moore. New York, W. H. Freeman and

Company, 2018.

Additionally, College Board Released Test Questions will be embedded within instruction and assessments. Online data

resources such as Wolfram Alpha, Gallup Poll, The Connecticut Crash Data Repository, and others yet to be identified

will also be sourced to provide students with relevant data to analyze.

COURSE PROJECTS For all units of study students will complete mini-projects, referred to as performance tasks, targeting the specific skills

and concepts students are learning. These performance tasks will help students develop their ability throughout the

statistical process. Each performance task will require students to utilize technology in order to perform analysis of

data and draw conclusions that they will be required to communicate clearly to readers. The culmination of which will

be students designing, executing, analyzing, and summarizing a statistical investigation of their own choosing.

https://www.wolframalpha.com/

http://www.gallup.com/home.aspx

https://ctcrash.uconn.edu/Login.action;jsessionid=D9C93F2075F8B282BC762F1D30EE99F2


UNIT 1: DATA ANALYSIS Pacing: 8 Blocks

Description

This unit will focus on the foundational skills for data analysis. Students will learn about the different types of variables that can be studied, the ways in which these variables are analyzed, and various ways to display data. Student will communicate observed patterns and summarize any conclusions that may be made from the data.

Learning Targets

Introduction

Identify the individuals and variables in a set of data.

Classify variables as categorical or quantitative. Section 1.1

Make and interpret bar graphs for categorical data.

Identify what makes some graphs of categorical data misleading.

Calculate marginal and joint relative frequencies from a two-way table.

Use bar graphs to compare distributions of categorical data.

Describe the nature of the association between two categorical variables. Section 1.2

Make and interpret dotplots, stemplots, and histograms of quantitative data.

Identify the shape of a distribution from a graph.

Describe the overall pattern (shape, center, and variability) of a distribution and identify any major departures from the pattern (outliers).

Compare distributions of quantitative data using dotplots, stemplots, and histograms. Section 1.3

Calculate the measures of center (mean, median) for a distribution of quantitative data.

Calculate the measures of variability (range, standard deviation, IQR) for a distribution of quantitative data.

Explain how outliers and skewness affect measures of center and variability.

Identify outliers using the 1.5 × 𝐼𝑄𝑅 rule.

Make and interpret boxplots of quantitative data.

Use boxplots and numerical summaries to compare distributions of quantitative data.

Technology Enhancements

Ti-84 Features to be embedded

Create, analyze, and compare histograms and boxplots of quantitative variables.

Calculate summary statistics for quantitative variables. Non-calculator Resources

Google Sheets to analyze two-way tables.

Google Sheets to create, analyze and compare histograms for quantitative variables.

Google Sheets to calculate summary statistics for quantitative variables.

One-Variable Statistical Calculator applet through the textbook

Assessments

Students will complete:

Formative assessments

End of Unit Assessment

AP Free-Response Practice Question

Categorical Variable Analysis Performance Task

Quantitative Variable Analysis Performance Task

Alignments Textbook Chapter 1

College Board I.A.1-4, I.B.1-4, I.C.1-4, 1.E.1-4

CCS ID.A.1-3, ID.B.5

http://digitalfirst.bfwpub.com/stats_applet/stats_applet_8_ovc.html

https://drive.google.com/drive/folders/17sVh65SehOUvMfpP8UqSue5bIpJRMPa-?usp=sharing

https://drive.google.com/drive/folders/1nHPg5SEG9bz4w2wlzfvfcLztCkHteNiy?usp=sharing

http://www.corestandards.org/Math/Content/HSS/ID/



UNIT 2: MODELING DISTRIBUTIONS OF DATA Pacing: 6 Blocks

Description This unit will focus on students describing the location of an individual observation within a distribution. Students will learn how to standardize individual values, how to model a distribution with a density curve, and begin to utilize the standard Normal model.

Learning Targets

Section 2.1

Find and interpret the percentile of an individual value in a distribution of data.

Estimate percentiles and individual values using a cumulative relative frequency graph.

Find and interpret the standardized score (z-score) of an individual value in a distribution of data.

Describe the effect of adding, subtracting, multiplying by, or dividing by a constant on the shape, center, and variability of a distribution of data.

Section 2.2

Use a density curve to model distributions of quantitative data.

Identify the relative locations of the mean and median of a distribution from a density curve.

Use the 68-95-99.7 Rule to estimate (i) the proportion of values in a specified interval, or (ii) the value that corresponds to a given percentile in a Normal distribution.

Find the proportion of values in a specified interval in a Normal distribution using Table A or technology.

Find the value that corresponds to a given percentile in a Normal distribution using Table A of technology.

Determine whether a distribution of data is approximately Normal from graphical and numerical evidence.



Examine the shape of normal distributions (normalpdf)

Calculate the proportion of z-values in a specified interval (normalcdf)

Calculate the z-score from a percentile (invNorm)

Create, interpret, and analyze a Normal probability plot Non-calculator Resources

Normal Density Curve applet through the textbook

Assessments





Normal Model Performance Task


College Board I.A, I.B.5, III.C.1-3

CCS ID.A.4

http://digitalfirst.bfwpub.com/stats_applet/stats_applet_7_norm.html

https://drive.google.com/drive/folders/1StfTIAKkngVHnN-B0XwsCd8lB_C8O1DS?usp=sharing



UNIT 3: DESCRIBING RELATIONSHIPS Pacing: 7 Blocks

Description

This unit will focus on investing the relationship between two quantitative variables. Students will learn how to create, analyze, and interpret scatterplots. Students will learn how to calculate and utilize a linear regression for extrapolation; addressing the dangers of doing such. Emphasis will be placed on ensuring students’ understand the differences between association, correlation, and causation.

Learning Targets

Section 3.1

Distinguish between explanatory and response variable for quantitative data.

Make a scatterplot to display the relationship between two quantitative variables.

Describe the direction, form, and strength of a relationship displayed in a scatterplot and identify unusual features.

Interpret the correlation.

Understand the basic properties of correlation, including how the correlation is influenced by outliers.

Distinguish correlation from causation. Section 3.2

Make predictions using regression lines, keeping in mind the dangers of extrapolation.

Calculate and interpret a residual.

Interpret the slope and y-intercept of a regression line.

Determine the equation of a least-squares regression line using technology or computer output.

Construct and interpret residual plots to assess whether a regression model is appropriate.

Interpret the standard deviation of the residuals and 𝑟2 and use these values to assess how well a least-squares regression line models the relationship between two variables.

Describe how the least-squares regression line, standard deviation of the residuals and 𝑟2 are influenced by outliers.

Find the slope and y-intercept of the least-squares regression line from the means and standard deviations of x and y and their correlation.



Create and analyze scatterplots.

Calculate, interpret, and analyze the least-squares regression line, including correlation.

Create and analyze residual plots for a linear model. Non-calculator Resources

Google Sheets to create scatterplots.

Google Sheets to determine a linear regression.

Two Variable Statistical Calculator applet from textbook.

Assessments





Least-squares Regression Performance Task


College Board I.D.1-5

CCS ID.B.6.A-C, ID.C.7-9

http://digitalfirst.bfwpub.com/stats_applet/stats_applet_16_tvc.html

https://drive.google.com/open?id=1M6oJvU_oBpX7EY3VlAWx3VOo1LALZR23HFREgHjQ4Ts




UNIT 4: COLLECTING DATA Pacing: 7 Blocks

Description

This unit will focus on the design of statistical studies and experiments. Students will learn how to utilize random sampling techniques to complete a sample survey that is representative of the population, as well as identifying sources of bias in sampling surveys. Students will also learn about the process of experimental design for investigating the relationship between two variables.

Learning Targets

Section 4.1

Identify the population and sample in a statistical study.

Identify voluntary response sampling and convenience sampling and explain how these sampling methods can lead to bias.

Describe how to select a simple random sample with technology or a table of random digits.

Describe how to select a sample using stratified random sampling and cluster sampling, distinguish stratified random sampling from cluster sampling, and give and advantage of each method.

Explain how undercoverage, nonresponse, question wording, and other aspects of a sample survey can lead to bias.

Section 4.2

Explain the concept of confounding and how it limits the ability to make cause-and-effect conclusions

Distinguish between an observational study and an experiment, and identify the explanatory and response variable in each type of study.

Identify the experimental units and treatments in an experiment.

Describe the placebo effect and the purpose of blinding in an experiment.

Describe how to randomly assign treatments in an experiment using slips of paper, technology, or a table of random digits.

Explain the purpose of comparison, random assignment, control, and replication in an experiment.

Describe a completely randomized design for an experiment.

Describe a randomized block design and a matched pairs design for an experiment and explain the purpose of blocking in an experiment.

Section 4.3

Explain the concept of sampling variability when making an inference about a population and how sample size affects sampling variability.

Explain the meaning of statistically significant in the context of an experiment and use simulation to determine if the results of an experiment were statistically significant.

Identify when it is appropriate to make an inference about cause and effect.

Evaluate if a statistical study has been carried out in an ethical manner.



Random number generation to assist in random sampling/assignment (randInt) Non-calculator Resources

Online Random Number Generators (option 1, option 2, option 3)

Simple Random Sample applet from the textbook.

Assessments




AP Free Response Practice Question

Sample Survey or Experimental Design Performance Task


College Board II.A.1-4, II.B.1-4, II.C.1-5, II.D

CCS IC.B.3, IC.B.5,

https://www.random.org/

http://www.randomnumbergenerator.com/

http://numbergenerator.org/randomnumberbetween1and10

http://digitalfirst.bfwpub.com/stats_applet/stats_applet_13_srs.html

http://www.corestandards.org/Math/Content/HSS/IC/



UNIT 5: PROBABILITY Pacing: 6 Blocks

Description This unit will focus on the laws of probability and calculating the probability of specific events. Students will learn the definition of probability and how to simulate chance events. Students will learn how to determine if events are disjoint and independent.

Learning Targets

Section 5.1

Interpret probability as a long-run relative frequency.

Use simulation to model chance behavior. Section 5.2

Give a probability model for a chance process with equally likely outcomes and use it to find the probability of an event.

Use basic probability rules, including the complement rule and the addition rule for mutually exclusive events.

Use a two-way table or Venn diagram to model a chance process and calculate probabilities involving two events.

Apply the general addition rule to calculate probabilities. Section 5.3

Calculate and interpret conditional probabilities.

Determine if two events are independent.

Use the general multiplication rule to calculate probabilities.

Use a tree diagram to model a chance process involving a sequence of outcomes and to calculate probabilities.

When appropriate, use the multiplication rule for independent events to calculate probabilities.



Probability Simulation App Non-calculator Resources

Probability applet through the textbook

Assessments





Probability Performance Task


College Board III.A.1-3, III.A.5

CCS CP.A.1-5, CP.B.6-9

http://digitalfirst.bfwpub.com/stats_applet/stats_applet_10_prob.html

http://www.corestandards.org/Math/Content/HSS/CP/

http://www.corestandards.org/Math/Content/HSS/CP/


UNIT 6: RANDOM VARIABLES Pacing: 7 Blocks

Description

This unit will continue the study of probability by focusing on probability distributions for random variables. Students will learn about discrete and continuous random variables how calculate and interpret the meaning of various statistics within these models. Students will also learn about the binomial model and geometric random variables in order to calculate the probability of various events.

Learning Targets

Section 6.1

Use the probability distribution of a discrete random variable to calculate the probability of an event.

Make a histogram to display the probability distribution of a discrete random variable and describe its shape.

Calculate and interpret the mean (expected value) of a discrete random variable.

Calculate and interpret the standard deviation of a discrete random variable.

Use the probability distribution of a continuous random variable (uniform or Normal) to calculate the probability of an event.

Section 6.2

Describe the effects of adding or subtracting a constant or multiplying or dividing by a constant on the probability distribution of a random variable.

Calculate the mean and standard deviation of the sum or difference of random variables.

Find the probabilities involving the sum or difference of independent Normal random variables.

Section 6.3

Determine whether the conditions for a binomial setting are met.

Calculate and interpret the probabilities involving binomial distributions.

Calculate the mean and standard deviation of a binomial random variable. Interpret these values.

When appropriate, use the Normal approximation to the binomial distribution to calculate probabilities.

Find probabilities involving geometric random variables.



Probability Simulation App

Examine and analyze probability distributions (create a histogram)

Calculate the mean and standard deviation of a probability model (1-Var Stats)

Calculate factorials, permutations, and combinations on the calculator.

Calculate a binomial probability (binompdf, binomcdf)

Calculate a geometric probability (geometpdf, geometcdf) Non-calculator Resources

Normal approximation to a binomial model applet through textbook.

Assessments





Random Variable Performance Task


College Board III.A.4, III.A.6, III.B.1-2

CCS MD.A.1-4, MD.A.5-7

http://digitalfirst.bfwpub.com/stats_applet/stats_applet_2_cltbinom.html

http://www.corestandards.org/Math/Content/HSS/MD/

http://www.corestandards.org/Math/Content/HSS/MD/


UNIT 7: INFERENCE ABOUT PROPORTIONS Pacing:

10 Blocks

Description

This unit will begin work on statistical inference. Students will be given an overview of sampling distributions, confidence intervals, and significance testing. Students will then apply this skills specifically to sample proportions and population proportions. Students will communicate their findings and conclusions using statistical vocabulary; interpreting what their calculations represent.

Learning Targets

Section 7.1

Distinguish between a parameter and a statistic.

Create a sampling distribution using all possible samples from a small population.

Use the sampling distribution of a statistic to evaluate a claim about a parameter.

Distinguish among the distribution of a population, the distribution of a statistic, and the sampling distribution of a statistic.

Determine if a statistic is an unbiased estimator of a population parameter.

Describe the relationship between sample size and the variability of a statistic. Section 7.2

Calculate the mean and standard deviation of the sampling distribution of a sample proportion �̂� and interpret the standard deviation.

Determine if the sampling distribution of �̂� is approximately Normal.

If appropriate, use a Normal distribution to calculate probabilities involving �̂�. Section 8.1

Identify an appropriate point estimator and calculate the value of a point estimate.

Interpret a confidence interval in context.

Determine the point estimate and margin of error from a confidence interval.

Use a confidence interval to make a decision about the value of a parameter.

Interpret a confidence level in context.

Describe how the sample size of a confidence level affect the margin of error. Section 8.2

State and check the Random, 10%, and Large Counts conditions for constructing a confidence interval for a population proportion.

Determine the critical value for calculating a C% confidence interval for a population proportion using a table or technology.

Construct and interpret a confidence interval for a population proportion.

Determine the sample size required to obtain a C% confidence interval for a population proportion using a table or technology.

Section 9.1

State appropriate hypotheses for a significance test about a population parameter.

Interpret a P-value in context.

Make an appropriate conclusion for a significance test.

Interpret a Type I and a Type II error in context. Give a consequence of each error in a given setting.

Section 9.2

State and check the Random, 10%, and Large Counts conditions for performing a significance test about a population proportion.

Calculate the standardized test statistic and P-value for a test about a population proportion.

Perform a significance test about a population proportion.



Calculate the confidence interval for a proportion (1-propZInt)

Complete a one-sample z test for a proportion (1-propZTest) Non-calculator Resources

Confidence interval applet from the textbook.

http://digitalfirst.bfwpub.com/stats_applet/stats_applet_4_ci.html


Reasoning of a statistical test applet from the textbook.

Assessments



Mid-Unit Assessment



Inference About Proportions Performance Task

Alignments Textbook Chapters 7, 8, 9

College Board III.D.1-3, III.D.6, IV.A.1-4, IV.B.1-2

CCS IC.A.1-2, IC.B.4-6

http://digitalfirst.bfwpub.com/stats_applet/stats_applet_15_reasoning.html




UNIT 8: INFERENCE ABOUT MEANS Pacing: 6 Blocks

Description

This unit will continue work on statistical inference. Students will apply the skills of sampling distributions, confidence intervals, and significance testing to sample means and population means. Students will communicate their findings and conclusions using statistical vocabulary; interpreting what their calculations represent.

Learning Targets

Section 7.3

Calculate the mean and standard deviation of the sampling distribution of a sample mean �̅� and interpret the standard deviation.

Explain how the shape of the sampling distribution of �̅� is affected by the shape of the population distribution and the sample size.

If appropriate, use a Normal distribution to calculate probabilities involving �̅�. Section 8.3

Determine the critical value for calculating a C% confidence interval for a population mean using a table or technology.

State and check the Random, 10% and Normal/Large Sample conditions for constructing a confidence interval for a population mean.

Construct and interpret a confidence interval for a population mean.

Determine the sample size required to obtain a C% confidence interval for a population mean with a specified margin of error.

Section 9.3

State and check the Random, 10%, and Normal/Large Sample conditions for performing a significance test about a population mean.

Calculate the standardized test statistic and P-value for a test about a population mean.

Perform a significance test about a population mean.

Use a confidence interval to make a conclusion about a two-sided test about a population parameter.

Interpret the power of a significance test and describe what factors affect the power of a test.



Calculate the critical value for a confidence interval for a mean (invT)

Calculate the confidence interval for a mean (TInterval)

Computing p-values from a t-distribtuion (tcdf)

Complete a one-sample t-test for the mean (T-Test) Non-calculator Resources

Statistical Power applet through textbook.

Assessments





Inference About Means Performance Task

Alignments Textbook Chapters 7, 8, 9

College Board III.D.7, IV.A.7, IV.B.4-5

CCS IC.A.1-2, IC.B.4-6

http://digitalfirst.bfwpub.com/stats_applet/stats_applet_9_power.html




UNIT 9: COMPARNG TWO POPULATIONS OR GROUPS Pacing: 7 Blocks

Description

This unit will focus on statistical inference for the difference between two groups. Students will revisit sampling distributions, confidence intervals, and significance test. However, this time students will be examining the difference between sample/population proportions or sample/population means.

Learning Targets

Section 10.1

Describe the shape, center, and variability of the sampling distribution of �̂�1 − �̂�2

Determine whether the conditions are met for doing inference about a difference between two proportions.

Construct and interpret a confidence interval for a difference between two proportions.

Perform a significance test about a difference between two proportions. Section 10.2

Describe the shape, center, and variability of the sampling distribution of �̅�1 − �̅�2

Determine whether the conditions are met for doing inference about a difference between two means.

Construct and interpret a confidence interval for a difference between two means.

Calculate the standardized test statistic and P-value for a test about a difference between two means.

Perform a significance test about a difference between two means. Section 10.3

Analyze the distribution of difference in a paired data set using graphs and summary statistitics.

Construct and interpret a confidence interval for a mean difference.

Perform a significance test about a mean difference.

Determine when it is appropriate to use paired t procedures versus two-sample t procedures.



Calculate a confidence interval for a difference in proportions (2-PropZInt)

Complete a significance test for a difference in proportions (2-PropZTest)

Calculate a two sample t interval (2-SampTInt)

Complete a two sample t Test (2-SampTTest) Non-calculator Resources n/a

Assessments




AP Free Response Practice Problem

Comparing Two Groups Performance Task


College Board III.D.4-5, IV.A.5, IV.A.7, IV.B.3, IV.B.5

CCS n/a


UNIT 10: INFERENCE FOR CATEGORCAL VARIABLES Pacing: 8 Blocks

Description In this unit, students will apply statistical inference to categorical variables. Students will learn how to apply chi-square tests to single events as well as two-way tables, drawing connections back to earlier topics in probability, such as conditional distributions and independence.

Learning Targets

Section 11.1

State appropriate hypotheses and compute the expected counts and chi-square test statistic for a chi-square test for goodness of fit.

State and check the Random, 10%, and Large Counts conditions for performing a chi-square test for goodness of fit.

Calculate the degrees of freedom and P-value for a chi-square test for goodness of fit.

Perform a chi-square test for goodness of fit.

Conduct follow-up analysis when the result of a chi-square test are statistically significant. Section 11.2

State appropriate hypotheses and compute the expected counts and chi-square test statistic for a chi-square test based on data in a two-way table.

State and check the Random, 10%, and Large Counts conditions for a chi-square test based on data in a two way table.

Calculate the degrees of freedom and P-value of a chi-square test based on data in a two-way table.

Perform a chi-square test for homogeneity.

Perform a chi-square test for independence.

Choose the appropriate chi-square test in a given setting.

Conduct follow-up analysis when the result of a chi-square test are statistically significant.



Finding P-values for chi-square tests (𝜒2cdf)

Chi-square test for goodness of fit (𝜒2GOF-Test)

Chi-square tests for two way tables (matrices & 𝜒2-Test) Non-calculator Resources n/a

Assessments




AP Free Response Practice Problem

Inference About Categorical Variables Performance Task

Alignments Textbook Chapters 11

College Board III.D.8, IV.B.6

CCS n/a


UNIT 11: MORE ABOUT REGRESSIONS Pacing: 8 Blocks

Description

In this unit, students will apply statistical inference to linear regressions. Students will learn how to apply confidence intervals and significance testing to the slope of a linear regression. Students will also learn how to apply transformations to non-linear relations in order to find a curve that models that data.

Learning Targets

Section 12.1

Check the conditions for performing inference about the slope 𝛽1 of the population (true) regression line.

Interpret the values of 𝑏𝑜, 𝑏1, 𝑠, and 𝑆𝐸𝑏1in context, and determine these values from

compute output.

Construct and interpret a confidence interval for the slope 𝛽1 of the population (true) regression line.

Perform a significance test about the slope 𝛽1 of the population (true) regression line.

Section 12.2

Use transformations involving powers and roots to find a power model that describes the relationship between two quantitative variables, and use the model to make predictions.

Use transformations involving logarithms to find a power model that describes the relationship between two quantitative variables, and use the model to make predictions.

Use transformations involving logarithms to find an exponential model that describes the relationship between two quantitative variables, and use the model to make predictions.

Determine which of several transformations does a better job of producing a linear relationship.



Calculate a confidence interval for the slope of a regression (LinRegTInt)

Perform a significance test from the slope of a regression (LinRegTInt)

Transforming to achieve linearity Non-calculator Resources n/a

Assessments





Inference About Regressions Performance Task

Alignments Textbook Chapters 12

College Board IV.A.8, IV.B.7

CCS n/a


Following the AP Exam,

UNIT 12: FINAL PROJECT Pacing: 7 Blocks

Description

In pairs or groups of three students will choose a topic of interest to investigate. Students will then,

Identify the question(s) that they intend to analyze.

Design either a sample survey or experiment in order to collect data for statistical analysis.

Execute their sample survey or experiment in order to collect data for analysis.

Analyze the data they have collected, including all statistical inference that may be appropriate.

Summarize any conclusions they reach through their statistical analysis.

Students will identify any further questions that arise from their initial investigation.

Students will present their finds to a group through a medium of their choosing (oral presentation, WeVideo, written report, poster, etc.)

Learning Targets

Synthesis of the learning objectives throughout the course. Varies with each project.


Varies with each project

Assessments

Students will complete final product that describes their project. This final product may be in a format of their choosing including, but not limited to, a written report, oral presentation, news broadcast, etc. This include demonstration of the requirements above.

Alignments Textbook Varies with each project

College Board Varies with each project

CCS Varies with each project


Appendix A: College Board Topic Outline


Appendix B: College Board Formula Sheet


Appendix B: College Board Tables


Appendix C: Content Specific Vocabulary (by Unit)

Unit 1

1.5 × 𝐼𝑄𝑅 rule for outliers, association, back-to-back stemplot, bar graph, bimodal, boxplot, categorical variable, conditional distribution, conditional relative frequencies, data analysis, distribution, dotplot, first quartile (𝑄1), five-number summary, frequency table, histogram, individual, inference, interquartile range (IQR), joint relative frequencies, marginal relative frequencies, mean, median, outlier, pie chart, quantitative variable, quartiles, range, relative frequency table, resistant, round off error, segmented bar graph, side-by-side bar graph, skewed, standard deviation (𝑠𝑥), statistics, stemplot, symmetric, third quartile (𝑄3), two-way table, uniform distribution, unimodal, variable, variance (𝑠2 𝑥)

Unit 2

68-95-99.7 Rule, Chebyshev’s inequality, cumulative relative frequency graph, density curve, mean of a density curve, median of a density curve, Normal curve, Normal distribution, Normal probability plot, outlier, percentile, standard Normal distribution, standard Normal table (Table A), standardized score (z-score)

Unit 3

coefficient of determination (𝑟2), correlation (𝑟), explanatory variable, extrapolation, influential observation, least-squares regression line, negative association, outlier in regression, positive association, predicted value, regression line, residual plot, residuals, response variable, scatterplot, slope, standard deviation of the residuals (𝑠), y-intercept

Unit 4

anonymity, bias, block, census, cluster sampling, comparison, completely randomized design, confidential, confounding, control, control group, convenience sampling, double-blind, experimental unit, experiments, factor, inference, inference about a population, inference about cause and effect, informed consent, institutional review board, level, margin of error, matched pairs design, nonresponse, observational study, placebo, placebo effect, population, random assignment, random sampling, randomized block design, replication, response bias, sample, sample survey, sampling variability, simple random sample, single-blind, statistically significant, stratified random sampling, subjects, treatment, undercoverage, voluntary response sampling, wording of questions

Unit 5

addition rule for mutually exclusive events, complement, complement rule, conditional probability, event, general addition rule, general multiplication rule, independent events, intersection, law of large numbers, multiplication rule for independent events, mutually exclusive, probability, probability model, sample space, simulation, tree diagram, union, Venn diagram

Unit 6

10% condition, binomial distribution, binomial random variable, binomial setting, continuous random variable, discrete random variable, factorial, geometric distribution, geometric probability formula, geometric random variable, geometric setting, independent random variables, Large Counts condition, mean (expected value) of a discrete random variable, Normal approximation to a binomial distribution, probability distribution, random variable, standard deviation of a discrete


random variable, variance of a discrete random variable

Unit 7 and

Unit 8

alternative hypothesis 𝐻𝑎, biased estimator, central limit theorem (CLT), confidence interval, confidence level C, critical value, fail to reject 𝐻𝑜, margin of error, Normal/Large sample condition, null hypothesis 𝐻𝑜, one-sample t interval for a mean, one-sample t test for a mean, one-sample z interval for a proportion, one-sample z test for a proportion, one-sided alternative hypothesis, P-value, parameter, point estimate, point estimator, power, random condition, reject 𝐻𝑜, sampling distribution, sampling distribution of the sample mean, sampling distribution of the sample proportion, sampling variability, significance level, significance test, standard error standardized test statistic, statistic, statistically significant, t distribution, two-sided alternative hypothesis, Type I error, Type II error, unbiased estimator, variability of a statistic

Unit 9

one sample t interval for a mean difference, paired data, paired t interval for a mean difference, paired t test for a mean difference, pooled or combined sample proportion, randomization distribution, sampling distribution of �̂�1 − �̂�2, sampling distribution of �̅�1 − �̅�2, two-sample t interval for a difference between two means, two-sample t test for the difference between two means, two-sample z interval for the difference between two proportions, two-sample z test for the difference between two proportions

Unit 10

chi-squared distribution, chi-squared test for goodness of fit, chis-squared test for homogeneity, chi-squared test for independence, chi-squared statistic, components, expected counts, Large Counts condition for chi-squared test, multiple comparisons, observed counts, one-way table

Unit 11

exponential model, population (true) regression line, power model, sample regression line (estimated regression line), sampling distribution of a slope 𝑏1, t interval for the slope B, t test for the slope, transforming

ledyard public schools ap statistics curriculum · molesky, jason m., et al. strive for a 5:...

Documents